[1] L Rudin,S Osher,E Fatemi.Nonlinear total variation based noise removal algorithms[J].Physica D:Nonlinear Phenomena,1992,60(1-4):259-268.
[2] Yiqiu Dong,Michael Hintermüller,M Monserrat Rincon-Camacho.Automated regularization parameter selection in multi-scale total variation models for image restoration[J].Journal of Mathematical Imaging and Vision,2011,40(1):82-104.
[3] 卢成武,宋国乡.带曲波域约束的全变差正则化抑噪方法[J].电子学报,2008,36(4):646-649. LU Cheng-wu,SONG Guo-xiang.Total variation regularization denoising method with constraint on curvelet-domain[J].Acta Electronica Sinica,2008,36(4):646-649.(in Chinese)
[4] M Bertalmio,V Caselles,B Rougé,et al.TV based image restoration with local constraints[J].Journal of Scientific Computing,2003,19(1-3):95-122.
[5] Buades A,Coll B,Morel J M.A non-local algorithm for image denoising[A].Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].San Diego:IEEE,2005.Vol.2:60-65.
[6] 郑钰辉,孙权森,夏德深.基于2DPCA的有效非局部滤波方法[J].自动化学报,2010,36(10):1379-1389. ZHENG Yu-Hui,SUN Quan-Sen,XIA De-Shen.Anefficient 2DPCA-based non-local means filter[J].Acta Automatica Sinica,2010,36(10):1379-1389.(in Chinese)
[7] G Gilboa,S Osher.Nonlocal operators with applications to image processing[J].SIAM Multiscale Modeling and Simulation,2007,7(3):1005-1028.
[8] 孙玉宝,韦志辉,吴敏,等.稀疏性正则化的图像泊松去噪算法[J].电子学报,2011,39(2):285-290. SUN Yu-bao,WEI Zhi-hui,WU Min,et al.Image poisson denoising using sparse representations[J].Acta Electronica Sinica,2011,39(2):285-290.(in Chinese)
[9] Chao Jia,Brian L Evans.Patch-based image deconvolution via joint modeling of sparse priors[A].Proceedings of IEEE International Conference on Image Processing[C].Brussels:IEEE,2011.681-684.
[10] M Aharon,et al.K-SVD:an algorithm for designing of overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[11] J Mairal,et al.Non-local sparse models for image restoration[A].Proceedings of IEEE International Conference on Computer Vision[C].Kyoto:IEEE,2009.2272-2279.
[12] Weisheng Dong,et al.Sparsity-based image denoising via dictionary learning and structural clustering[A].Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C].Colorado Springs:IEEE,2011.457-464.
[13] M Elad,M Aharon.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[14] P Chatterjee,P Milanfar.Clustering-based denoising with locally learned dictionaries[J].IEEE Transactions on Image Processing,2009,18(7):1438-1451.
[15] L Zhang,W Dong,D Zhang,et al.Two-stage image denoising by principal component analysis with local pixel grouping[J].Pattern Recognition,2010,43(4):1531-1549.
[16] G Gilboa,S Osher.Nonlocal operators with applications to image processing[J].SIAM Multiscale Modeling and Simulation,2007,7(3):1005-1028.
[17] X Zhang,M Burger,X Bresson,et al.Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J].SIAM Journal on Imaging Sciences,2010,3(3):253-276.
[18] Xin Li.Fine-granularity and spatially-adaptive regularization for projection-based image deblurring[J].IEEE Transactions on Image Processing,2011,20(4):971-983.
[19] T Goldstein,S Osher.The split Bregman method for L1 regularized problems[J].SIAM Journal on Imaging Sciences,2009,2(2):323-343.
[20] I Daubechies,M Defriese,C DeMol.An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J].Communications on Pure and Applied Mathematics,2004,57(11):1413-1457. |